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Published on: December 9, 2015
The changing demographic pattern of multiple sclerosis epidemiology
Nils Koch-Henriksen1, Per Soelberg Sørensen
1Department of Neurology, Aarhus University Hospital in Aalborg, Aalborg, Denmark. koch-henriksen@stofanet.dk
Multiple sclerosis (MS) incidence and prevalence are increasing globally, challenging traditional geographical patterns and suggesting lifestyle changes in females may be a factor. Complex gene-environment interactions now explain MS distribution better than simple cause-effect models.
Area of Science:
- Epidemiology
- Neuroscience
- Genetics
Background:
- Multiple sclerosis (MS) distribution varies geographically, influenced by genetic and environmental factors.
- Traditional epidemiological models for MS are being re-evaluated due to new insights.
- Factors like diagnostic accuracy, survival rates, and ascertainment bias can affect prevalence and incidence data.
Purpose of the Study:
- To analyze temporal and geographical trends in MS prevalence and incidence.
- To investigate the validity of the latitudinal gradient theory for MS.
- To explore potential changes in MS incidence related to sex and lifestyle.
Main Methods:
- Comprehensive literature search on MS epidemiology.
- Meta-regression analyses to identify trends in prevalence and incidence.
- Examination of geographical variations and temporal changes.
Main Results:
- A near-universal increase in MS prevalence and incidence over time was observed.
- The established latitudinal gradient of MS incidence is challenged in Europe and North America but persists in Australia and New Zealand.
- A general increase in MS incidence among females is suggested, warranting further investigation into lifestyle factors.
Conclusions:
- MS epidemiology is complex, with increasing global incidence and prevalence.
- The role of gene-environment and gene-gene interactions is crucial for understanding MS distribution.
- Future research should focus on lifestyle factors, particularly in females, and re-evaluate traditional epidemiological models.
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